BACKGROUND Acinic cell carcinoma(ACC)is a malignant epithelial neoplasm that commonly occurs in the parotid gland.It is known to have a high recurrence rate and the potential to metastasize to the lung or cervical lymph nodes.However,few cases of ACC with bone metastasis have been reported in the medical literature.CASE SUMMARY The clinical significance of this case report lies in the unique site of occurrence of the metastasis:To the best of our knowledge,this report is the only literature documenting ACC arising in a shoulder mass.CONCLUSION Unusual presentations of uncommon malignancies can present diagnostic challenges for both surgeons and histopathologists.It is important to be aware of these rare occurrences in order to provide the best possible treatment for patients.
BACKGROUND Lung cancer(LC)is the leading cause of malignancy-related deaths worldwide.The most common sites of metastasis include the nervous system,bone,liver,respiratory system,and adrenal glands.LC metastasis in the parotid gland is very rare,and its diagnosis presents a challenge.Here,we report a case of parotid metastasis in primary LC.CASE SUMMARY The patient was a 74-year-old male who was discovered to have bilateral facial asymmetry inadvertently two years ago.The right earlobe was slightly swollen and without pain or numbness.Computed tomography(CT)examination showed bilateral lung space-occupying lesions.Pulmonary biopsy was performed and revealed adenocarcinoma(right-upper-lung nodule tissue).Positron emission tomography-CT examination showed:(1)Two hypermetabolic nodules in the right upper lobe of the lung,enlarged hy-permetabolic lymph nodes in the right hilar and mediastinum,and malignant space-occupying lesion in the right upper lobe of the lung and possible metastasis to the right hilar and mediastinal lymph nodes;and(2)multiple hypermetabolic nodules in bilateral parotid glands.Parotid puncture biopsy was performed considering lung adenocarcinoma metastasis.Gene detection of lung biopsy specimens revealed an EGFR gene 21 exon L858R mutation.CONCLUSION This case report highlights the challenging diagnosis of parotid metastasis in LC given its rare nature.Such lesions should be differentiated from primary tumors of the parotid gland.Simple radiological imaging is unreliable,and puncture biopsy is needed for final diagnosis of this condition.
Purpose: This study describes a machine-learning approach utilizing patients' anatomical changes to predict parotid mean dose changes in fractionated radiotherapy for head-and-neck cancer, thereby facilitating plan adaptation decisions. Methods: Parotid mean dose changes during treatment sessions are assumed to correlate with patients’ anatomical changes, quantified by 65 geometrical features in four sets. SET1 is the parotid volumetric changes;SET2 is the distance changes from the parotid to the PTV;SET3 is the length of beam path changes between the parotid and skin near the neck;SET4 is the distance changes from the parotid to the two bony landmarks—the dens of the C2 and tip of the basilar part of the occipital bone. The introduced landmarks in SET4 are used as surrogates for the PTV in SET2 due to PTV’s unavailability at the simulation stage. Signed Euclidean distance is applied to quantify the distance and beam path length. A decision tree classifier to predict an x% increase in parotid mean dose is developed. In a study involving 18 patients (36 parotids) previously treated with adaptive radiotherapy, a leave-one-out cross-validation combined with enumerating 4 combinations of the 65 geometrical features is used to find a feature subset maximizing classifier’s accuracy. The classifier’s accuracy, with and without SET2’s PTV features inclusion, is evaluated to determine the SET4’s bony landmark surrogate feasibility. Results: Under x = 5% (or x = 10%) parotid mean dose increase: without SET2’s PTV features inclusion, one beam path feature from SET3 and one bony landmark feature from SET4 yield maximal accuracy of 86.1%, which is a 30.5% (19.4%) improvement over prevalence = 55.6% (66.7%);TPR = 87.5% (75%), TNR = 85% (91.7%), PPV = 82.3% (81.8%) and NPV = 89.5% (88%). With SET2’s PTV features inclusion, accuracy increases from 86.1% to 91.6%. Conclusion: Under the current 18 enrolled patients’ data, we found that the introduced SET4’s bony landmarks are feasible surrogates for the SET2�
